InquireMobile

Dataset for InquireMobile: Teaching VLM-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning (ACL 2026).

InquireMobile is an interactive mobile GUI agent that actively seeks human confirmation at critical decision points instead of acting autonomously. This dataset contains:

• InquireBench evaluation tasks (instruction + category + intent), and
• the training trajectories used for supervised fine-tuning (SFT) and reinforcement fine-tuning (GRPO / RLVR).

Project page: https://bit-aqh.github.io/InquireMobile/homepage/ Paper: https://aclanthology.org/2026.acl-long.1487/

Contents

The dataset is organized as:

• data/ — all JSONL data files (see below).
• images/ — screenshots referenced by the data files (see Images).

All data files are line-delimited JSON (JSONL) unless noted otherwise.

Evaluation tasks (InquireBench)

File	Description
tasks_en.jsonl, tasks_zh.jsonl	Core evaluation task instructions (English / Chinese).
tasks_en_extended.jsonl	Extended English task set.
tasks_{en,zh}_huawei.jsonl, tasks_{en,zh}_simulator.jsonl	Task subsets for the Huawei device / simulator environments.
tasks_zh_huawei_7.jsonl	Huawei-device subset (7 tasks).

Task schema:

{
  "instruction": "Open WeChat, browse my Moments ...",
  "apps": ["wechat"],
  "category": "privacy",
  "language": "en",
  "need_login": true,
  "intension": "Open WeChat, browse my Moments ..."
}

Training data

File	Description
interactive_sft.jsonl	Stage-1 SFT trajectories (conversation format).
interactive_grpo_401408.jsonl	Stage-2 GRPO / RLVR trajectories (401408 max-pixels).
general_grpo_5k.jsonl	General GRPO training data (5k).
sft_general.jsonl	SFT data (general).
sft_pos.jsonl	SFT data (positive samples).

Instruction pool

File	Description
instructions.jsonl	Raw instruction pool (Chinese + English), with optional image links to the instruction screenshots.

GRPO / trajectory schema:

{
  "goal": "...",
  "action": "...",
  "bbox": [x1, y1, x2, y2],
  "width": 1080,
  "height": 1920,
  "resized_width": 432,
  "resized_height": 768,
  "img_list": ["..."],
  "system": "...",
  "query": "..."
}

SFT schema:

{
  "conversations": [
    {"from": "system", "value": "..."},
    {"from": "human", "value": "<image>..."},
    {"from": "gpt", "value": "..."}
  ],
  "images": ["..."]
}

Images

Images are stored under images/ and are referenced by relative paths from the data files (relative to the dataset root).

Directory	Description
images/trajectory/	Stage-2 GRPO rollout screenshots (UUID-named), referenced by img_list in the GRPO data.
images/instruction/	The 72 English instruction screenshots (1.png…72.png), referenced by the images field of the SFT data and by interactive_added.jsonl.

The data files reference images by their basename (UUID) or number (N.png); map these to the directories above when reproducing training/evaluation.

Usage

import json

with open("data/tasks_en.jsonl", encoding="utf-8") as f:
    tasks = [json.loads(line) for line in f]

Or download directly from Hugging Face:

huggingface-cli download PG23/InquireMobile --repo-type dataset --local-dir ./InquireMobile-data

License

• Data: CC BY 4.0

Citation

@inproceedings{ai-etal-2026-inquiremobile,
    title = "{I}nquire{M}obile: Teaching {VLM}-based Mobile Agent to Request Human Assistance via Reinforcement Fine-Tuning",
    author = "Ai, Qihang  and
      Bu, Pi  and
      Cao, Yue  and
      Wang, Yingyao  and
      Gu, Jihao  and
      Xing, Jingxuan  and
      Zhu, Zekun  and
      Jiang, Wei  and
      Zheng, Zhicheng  and
      Song, Jun  and
      Jiang, Yuning",
    booktitle = "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.acl-long.1487/",
    doi = "10.18653/v1/2026.acl-long.1487",
    pages = "32241--32255"
}